Efficient Irrigation Water Management Research Articles

A Review of Precision Irrigation Water-Saving Technology under Changing Climate for Enhancing Water Use Efficiency, Crop Yield, and Environmental Footprints

Water is considered one of the vital natural resources and factors for performing short- and long-term agricultural practices on Earth. Meanwhile, globally, most of the available freshwater resources are utilized for irrigation purposes in agriculture. Currently, many world regions are facing extreme water shortage problems, which can worsen if not managed properly. In the literature, numerous methods and remedies are used to cope with the increasing global water crises. The use of precision irrigation water-saving systems (PISs) for efficient water management under climate change is one of them and is a highly recommended approach by researchers. It can mitigate the adverse effects of changing climate and help enhance water use efficiency, crop yield, and environmental footprints. Thus, the present study aimed to comprehensively examine and review PISs, focusing on their development, implementation, and positive impacts on sustainable water management. In addition, we searched the literature using different online search engines and reviewed and summarized the main results of the previously published papers on PISs. We discussed the traditional irrigation method and its modernization for enhancing water use efficiency, PIS monitoring and controlling, architecture, data sharing communication technologies, the role of artificial intelligence for irrigation water-saving, and the future prospects of the PIS. Based on the brief literature review, the present study concluded that the future of PISs seems bright, driven by the need for efficient irrigation water management systems, technological advancements, and increasing environmental awareness. As the water scarcity problem intensifies due to climate change and population growth, the PIS is poised to play a critical role in optimizing and modernizing water usage, increasing water use efficiency, and reducing environmental footprints, thus ensuring sustainable agriculture development.

Leaf Area Index Estimation of Fully and Deficit Irrigated Alfalfa through Canopy Cover and Canopy Height

In arid and semiarid regions, crop production has high irrigation water demands due to low precipitation. Efficient irrigation water management strategies can be developed using crop growth models to assess the effect of different irrigation management practices on crop productivity. The leaf area index (LAI) is an important growth parameter used in crop modeling. Measuring LAI requires specialized and expensive equipment not readily available for producers. Canopy cover (CC) and canopy height (CH) measurements, on the other hand, can be obtained with little effort using mobile devices and a ruler, respectively. The objective of this study was to determine the relationships between LAI, CC, and CH for fully and deficit-irrigated alfalfa (Medicago sativa L.). The LAI, CC, and CH measurements were obtained from an experiment conducted at the Valley Road Field Lab in Reno, Nevada, starting in the Fall of 2020. Three irrigation treatments were applied to two alfalfa varieties (Ladak II and Stratica): 100%, 80%, and 60% of full irrigation demands. Biweekly measurements of CC, CH, and LAI were collected during the growing seasons of 2021 and 2022. The dataset was randomly split into training and testing subsets. For the training subset, an exponential model and a simple linear regression (SLR) model were used to determine the individual relationship of CC and CH with LAI, respectively. Also, a multiple linear regression (MLR) model was implemented for the estimation of LAI with CC and CH as its predictors. The exponential model was fitted with a residual standard error (RSE) and coefficient of determination (R2) of 0.97 and 0.86, respectively. A lower performance was obtained for the SLR model (RSE = 1.03, R2 = 0.81). The MLR model (RSE = 0.82, R2 = 0.88) improved the performance achieved by the exponential and SLR models. The results of the testing indicated that the MLR performed better (RSE = 0.82, R2 = 0.88) than the exponential model (RSE = 0.97, R2 = 0.86) and the SLR model (RSE = 1.03, R2 = 0.82) in the estimation of LAI. The relationships obtained can be useful to estimate LAI when CC, CH, or both predictors are available and assist with the validation of data generated by crop growth models.

Effect of super absorbent hydrogel on hydro-physical properties of soil under deficit irrigation

Due to water scarcity challenges, efficient management of irrigation water is becoming crucial. Water use efficiency (WUE) involves increasing crop productivity without increasing water consumption. This study was carried out to study the effect of hydrogel, deficit irrigation and soil type on WUE, soil hydro-physical properties and lettuce productivity. For this purpose, four irrigation treatments (100%, 85%, 70% and 60% of full irrigation requirements), four hydrogel concentrations (0, 0.1, 0.2 and 0.3% w/w) and three soil textural classes (clay, loamy sand, and sandy-clay soil) were conducted in pot experiment at open field during two consecutive seasons. The results revealed that crop growth parameters and soil hydro-physical properties were significantly affected by hydrogel application rates. Hydrogel addition significantly enhanced head fresh and dry weights, chlorophyll content, number of leaves and WUE. Application of hydrogel at 0.3% and 85% of irrigation requirements achieved the highest WUE without significant yield reductions. Changes in the studied hydro-physical properties of soil were more dependent on soil texture and hydrogel application rate than on the amount of irrigation water. The significant decrease in soil saturated hydraulic conductivity and bulk density confirms that super absorbent hydrogels could be recommended to improve soil water retention and enhance water use efficiency under deficit irrigation conditions.

Shaping India's climate future: A perspective on harnessing carbon credits from agriculture

Human activities are responsible for emitting greenhouse gases (GHGs) that contribute to global warming and climate change. As the world's second-largest producer of staple food and the third-largest emitter of GHGs, India has been witnessing an increase in demand for food and energy, resulting in increased emissions. Thus, to achieve net carbon neutrality by 2070, India must focus urgently on climate change mitigation. Its agriculture sector has the potential to transition from being a net emitter to a net absorber of GHGs by adopting sustainable farming practices such as zero tillage, laser-assisted precision land leveling, direct seeding of rice, intercropping, biochar application, use of solar energy, and more efficient management of irrigation water, soil nutrients, livestock feed, and manure. To incentivize climate consciousness, a voluntary carbon credit trading system could be utilized in agriculture, supported by a measurement, monitoring, reporting, and verification platform. This system would also bring about social, environmental, and financial co-benefits for its stakeholders. Specifically, the agriculture sector could substantially reduce the country's annual emissions by 84% from 2019 to 2070. But to realize their potential, the carbon markets must overcome the limitations currently set by policy, economic, cultural, and biophysical factors.

Estimating irrigation requirement and scheduling for major crops: Case study in Kampong Thom, Cambodia

The situation is the outcome of both increased water demand and climate change, the shortage of water resources is increasing in Cambodia. In this regard, efficient irrigation water management for vegetable production is a crucial need for farmers. The main crops farmed in Chroab commune, Santuk district, Kampong Thom province, Cambodia, are cabbage, potato, and tomato. This article uses the CROPWAT model to predict the water requirement. The pressure plant was used to measure the soil’s soil water capacity, and the findings ranged from 45.65 to 234.159 mm/m. The analysis showed that the overall CWR for crop water for the entire growing season for cabbage, potato, and tomato was 589.4 mm/165 days; 442.5 mm/130 days; and 585.4 mm/145 days, respectively. The findings of the study contribute to investigating the irrigation water requirements with scientific soil and climate inputs.

Preliminary results of irrigation management for mango using LSTM neural networks and IoT

Mango cultivation in Colombia faces the impact of regional climate variability. To improve fruit development and minimize environmental and economic effects, it is necessary to implement efficient irrigation and appropriate water management technologies. In this study, we developed a trend forecasting system based on an LSTM neural network and technologies such as ThingsBoard, LoRA, and MQTT. The aim was to improve mango irrigation practices through informed decisions based on monitoring and predicting matric potential and evapotranspiration variables. This article describes the development and application of the system for mango irrigation management. Results validate the effectiveness of the proposed system for mango cultivation, with RMSE indices of 1.56 and 0.0019 and determination coefficients (R2) of 0.9989 and 0.9971 for matric potential and evapotranspiration, respectively. These findings support enhancing growth conditions and promoting sustainable practices. Despite data availability limitations, the system's efficacy in prediction and irrigation management demonstrates significant potential to maximize productivity and reduce the environmental and economic impacts of inadequate water management.

Responses of Tomato Crop and Water Productivity to Deficit Irrigation Strategies and Salinity Stress in Greenhouse

Saudi Arabia faces water scarcity and inadequate sustainable sources, particularly in agriculture, necessitating efficient irrigation water management to improve productivity amidst rising demand. The study investigated the impact of irrigation levels and water salinity on tomato plants in greenhouses, covering four irrigation levels (100%, 80%, 60%, and 40% of ETc) and three water sources (FW (0.9 dS·m−1), SW (3.6 dS·m−1) and MW (2.25 dS·m−1)). Salinity impacts crop yield, physiological responses, and fruit quality. The photosynthesis, stomatal conductance, transpiration, and chlorophyll content decrease with MW and SW, negatively affecting morphological characteristics. For MW, it was recommended to apply 60% deficit irrigation with a yield of 98 kg·ha−1, and water productivity (WP) improved to 21.93 kg·m−3 compared to 13.65 kg·m−3 at full irrigation (FI). In SW, 80% irrigation was suggested, as there was no significant difference in yield compared to FI. For FW, 60% deficit irrigation produced the best water conservation (104.58 kg·ha−1 yield and 23.19 kg·m−3 WP), while FI produced the highest yield per unit area (123.48 kg·ha−1 yield and 16.51 kg·m−3 WP). Nonetheless, greater water and salinity stress was associated with increased fruit quality measures such as total acidity, vitamin C, and soluble solids. The results show that implementing deficit irrigation with salinity strategies in greenhouse tomatoes could improve crop adaptability, yield, and water productivity in the face of water scarcity and salinity variability.

Estimation of actual evapotranspiration using the simplified-surface energy balance index model on an irrigated agricultural farm

Evapotranspiration (ET) plays a crucial role in the energy and water balance of agricultural ecosystems and is a vital component of the hydrological cycle. Efficient irrigation water management relies on accurate spatiotemporal coverage of crop ET across a farm. Thanks to the availability of multi-temporal high-resolution satellite datasets and remote sensing-based surface energy balance models, near-real-time estimation of ET is now possible. This study utilized Landsat 8/9 data to estimate ET using the simplified surface energy balance index (S-SEBI) model, which was then compared to eddy covariance measurements over a semi-arid agricultural farm in New Delhi, India during the post-monsoon periods of 2021-22 and 2022-23. The S-SEBI model predicted daily ET from Landsat 8/9 data with an average correlation coefficient and RMSE of 0.89 and 0.79 mm/day, respectively. The spatiotemporal map was also used to evaluate the model's performance, and it could accurately differentiate between ET over dryland crops and well-irrigated wheat fields on the farm. Despite underestimating ET (0.51 mm/day) during the initial growing season (Nov-Dec) and overestimating it (0.73 mm/day) during mid-season (Feb-Mar), the S-SEBI model can still be an operational tool for mapping ET with high accuracy and sufficient variation across pixels, making it an ideal option for incorporating into irrigation scheduling.

Determination of Crop Water Requirement and Crop Coefficient at Different Growth Stages of Tomato by Using Weighing Type Lysimeter in Raichur Region

The water requirement for any cropping system is important consideration for designing and managing irrigation systems. Water requirement of crops varies substantially over the growing season mainly due to variation in crop cover and climatic conditions. For estimating crop water requirement of tomato crop, we have taken field experiment in weighing lysimeter for three seasons rabi - (7th September 2021 to 5th January 2022); Summer - (15th January 2022 to 15th May 2022) and Kharif - (6th June 2022 to 4th October 2022). The crop water requirement helps us to develop the crop coefficients for various growth stages (initial, development, mid and late season) for particular climatic conditions. Further, with exact crop coefficients derived from weighing type lysimeter will be helpful for determining water requirement. The results showed that the crop evapotranspiration (ETc) for initial, development, mid-season and late-season being 22.76, 110.71, 173.47 and 68.71 mm, respectively. With respect to crop coefficient (Kc), it is estimated to be 0.38, 0.64, 1.12 and 0.66 for initial, development, mid-season and late season stages, respectively during rabi season. The crop evapotranspiration (ETc) for initial, development, mid-season and late-season being 26.53, 164.76, 235.56 and 121.51 mm, respectively. With respect to crop coefficient (Kc), it is estimated to be 0.56, 0.87, 1.22 and 0.78 for initial, development, mid-season and late season stages, respectively during summer season. The crop evapotranspiration (ETc) for initial, development, mid-season and late-season being 32.31, 131.38, 173.51 and 60.13 mm, respectively. With respect to Kc, it is estimated to be 0.36, 0.77, 1.13 and 0.74 for initial, development, mid-season and late season stages respectively during kharif season. The measured Kc values were significantly different from the FAO-56 reported values. Therefore, local calibration of crop coefficients is an essential for efficient irrigation water management and precise water applications.

A VBA-Based Field Water Balance Model for Efficient Irrigation Water Management of Corn (Zea mays L.)

A field water balance model for efficient irrigation water management of corn was developed using Excel VBA. The model consists of five sub-components or modules, namely, (1) a plant subcomponent, (2) an effective rainfall subcomponent, (3) an evapotranspiration subcomponent, (4) a soil water dynamics subcomponent for the modeling of water flow into and within the soil layers, and (5) an irrigation subcomponent for the estimation of the required amount and timing of irrigation. The model was calibrated and validated using observed data from field experiments and the results showed a reasonably good agreement between the observed and simulated soil moisture values (MAE = 5.76 mm to 12.00 mm, RMSE = 6.83 mm to 13.12 mm, NRMSE = 0.102 to 0.196, and NSE = 0.37 to 0.90). The simulations emphasized that a significant amount of water savings can be achieved when rainfall is properly accounted for in managing water in the field, and that the frequency of rainfall occurrences is as important as the magnitude of rainfall received by the crops. The wide-ranging user-friendliness and simplicity of the model developed in this study can pave the way to eliminating the barriers which cause farmers to resist advancements in their farming practices as the model can easily be used not only by researchers and scientists but also by farmers, especially those with basic knowledge of spreadsheets.

Estimation of Crop Water Requirements and Irrigation Scheduling for Major Crops Grown in India's North-Eastern Region

The water demand of some agricultural crops grown in Assam was assessed in this research. To estimate the crop water requirements of potato, chili, and brinjal, the FAO-CROPWAT model was used. The climatic parameters like rainfall, solar radiation, wind speed, sun hours, relative humidity, minimum and maximum temperature were collected for 30 years from the Indian Meteorological Department (IMD) to estimate the crop water requirement (CWR). The reference evapotranspiration (ETo) was computed by using the Food and Agriculture Organization (FAO)-recommended Penman-Monteith method and was found to vary from 1.84 mm/day to 4.49 mm/day. The calculated ETo was further used to compute the CWR, the net irrigation requirement (NIR), and the gross irrigation requirement (GIR) to plan for irrigation application during the growing stages of the selected crops. The total CWR of potato, chili, and brinjal was estimated to be 260.6 mm, 262.1 mm, and 274.2 mm, respectively, and the total NIR of potato, chili, and brinjal sowed on the 1st of October was found to be 162.8 mm, 149.0 mm, and 167.2 mm, respectively, with an irrigation efficiency of 70%. In December, the CWR and NIR of potato and brinjal were found to be the highest. The month with the least effective rainfall (8.5 mm) was December. The findings of the study showed that efficient irrigation water management and irrigation scheduling can be achieved by using the CROPWAT model to maximize crop yield and minimize crop water stress.

A Low-Cost Wireless Sensor for Real-Time Monitoring of Water Level in Lowland Rice Field under Alternate Wetting and Drying Irrigation

The use of wireless sensors for real-time monitoring of field water level would greatly facilitate the application of alternate wetting and drying (AWD), an irrigation water management technique proven to result to significant water savings and reduced methane emissions in lowland rice production systems. However, most of the commercially available wireless sensors are generally costly. This study developed a low-cost wireless sensor that can perform real-time monitoring of water depth and surface temperature in lowland rice fields under an AWD irrigation regime. The sensor is composed mainly of an ultrasonic depth sensor, a waterproof temperature sensor, a humidity sensor, and a Wi-Fi-enabled microcontroller enclosed in a PVC cap that can be mounted in AWD pipes. The sensor was tested under laboratory, pseudo-field conditions and actual field conditions. Results showed a relatively high degree of agreement between sensor and manual measurements of water depth under all testing conditions, with the error ranging from only 5.2% to 6.6% and RMSE of 5.0 mm to 13.5 mm. The performance of the low-cost sensor also proved to be comparable with that of the high-end sensor, exhibiting practically similar measurement accuracy and higher precision. The wireless sensor developed in this study can provide a low-cost alternative to the high-cost and high-end sensors and other commercially available counterparts for efficient irrigation water management in lowland crop production systems during water-scarce conditions induced by climate change and climate variability.

Adapting Irrigation Strategies to Mitigate Climate Change Impacts: A Value Engineering Approach

Water scarcity and climate change are posing new challenges to irrigation management. Climate change increases water demand and decreases crop yields. The aim of this paper is to propose a framework to select the most efficient irrigation strategy to mitigate the impacts of climate change and achieve food security. Value engineering (VE) methodology is utilized to assure the functionality of the strategy and add an element of creativity while creating the value alternatives. The life cycle cost (LCC) technique is utilized to provide the optimum irrigation strategy from an economic perspective. The findings showed three different value alternatives for different crops, soil types, and irrigation systems. This paper contributes to the current state of knowledge by a) utilizing the Value Engineering methodology in irrigation management studies; b) evaluating irrigation strategies to ensure efficient irrigation water management; and c) providing policymakers with a tool to incorporate the added value and functionality into their policies regarding irrigation water.

Hydraulic Performance Evaluation of Low-Cost Gravity-Fed Drip Irrigation Systems Under Constant Head Conditions

The use of drip irrigation for upland crop production has gained popularity in developing countries due to its relatively high water-use efficiency. To maximize the efficiency of this irrigation method, it is important to evaluate its hydraulic performance in terms of emitter discharge and water distribution uniformity under various operating heads. In this study, the hydraulic performance of two locally available low-cost drip irrigation kits -referred to in this study as Drip Kit A and Drip Kit B - was assessed and compared under constant head conditions. Both drip kits are composed of a 1000-L intermediate bulk container (IBC) tank, 10-m submain line, 20-m lateral lines spaced 0.75 m apart, and cascade labyrinth emitters spaced 0.30 m apart along the laterals. Operating heads of 2.5, 3.5, 4.0, 4.5, 5.0, 5.5 m were used and maintained throughout the trials with the use of an overflowing tank. The submain and lateral lines were levelled (0% slope). Sampling of discharge rates was done for one-third of the total number of emitters for each of the tests performed. Results showed that the overall system emitter discharge rate generally increases with increasing operating head. Moreover, emitters farther from the submain line generated relatively lower discharge rates than those closer to it. Analysis of the experimental data showed that the Christiansen’s coefficient of uniformity (CU), emission uniformity (EU), and coefficient of variation (CV) ranged from 97.5 to 98.5, 95.9 to 97.7, and 0.02 to 0.04 for the drip systems tested. Further statistical analysis also showed that varying the operating heads does not have a statistically significant effect (α=5%) on the CU, EU, and CV of the system. Based on the results, Drip Kit B performed better than Drip Kit A in terms of water distribution uniformity. However, the latter could generate higher emitter discharge rates than the former. For both drip kits, an operating head of 2.5-m is recommendable for a 200 m2 plot, from the practical standpoint. This study has generated important empirical data that could serve as a basis for maximizing the performance efficiency of locally available drip irrigation systems for a more efficient irrigation water management.

Development of Low-cost Soil Moisture Monitoring System for Efficient Irrigation Water Management of Upland Crops

With the advancement of information and communication technology, various types of soil moisture sensors have been developed. Coupled with data loggers, these sensors could prove useful in monitoring soil moisture in upland crop production areas which in turn could be used for efficient irrigation water management. However, most of these sensors are costly and unaffordable to most farmers in developing countries. Hence, a low-cost soil moisture monitoring system intended to facilitate irrigation water management in upland crop production systems was developed in this study. The device was built with a capacitive soil moisture sensor, an ESP8266 Wi-Fi mini board, and a datalogging shield with RTC. Soil moisture measurements are transmitted via ESP-NOW to a server which also uses an ESP8266 Wi-Fi mini board. The low-cost soil moisture monitoring system was evaluated based on its measurement of volumetric water content and transmission of data via ESP-NOW. The performance of the capacitive soil moisture sensor was compared with the ICT International MP306 soil moisture sensor. Statistical analyses showed that volumetric water contents measured by the capacitive soil moisture sensor are comparable to those of the MP306 soil moisture sensor, thus conceived as a low-cost alternative to the high-end sensor. Moreover, test results on the range of ESP-NOW showed that data can be successfully transmitted over long distances. With regards to this, the low-cost soil moisture monitoring system may be integrated with other technologies to enable irrigation scheduling and automation for efficient irrigation water management in upland crop production systems.

Development of a real-time wireless sensor network-based information system for efficient irrigation of upland and lowland crop production systems

This study developed a real-time web-and WSN-based information system for efficient irrigation water management and automation of drip-irrigated upland crop and intermittently-irrigated lowland crop production systems. The web-based system uses Flutter and DART to accommodate multiple end user platforms, while the WSN-based system uses state-of-the-art hardware and sensors for real-time monitoring of soil moisture, water level and weather conditions. The sensors are wirelessly connected in a low-power mesh network that sends data to a central server. The sensor readings are uploaded to the web application via MQTT, which generates charts and graphs for data analysis. The sensor readings compared well with measurements from conventional instruments. The system in this study provides a sustainable solution for improving irrigation efficiencies under both upland and lowland crop production systems, in minimizing water losses and in improving the overall agricultural crop productivity.

Development of mobile application for wireless sensor networks for efficient irrigation water management

The evolution of smart phones has necessitated the development of mobile applications designed to perform a wide variety of functions. In the field of agriculture, mobile applications are currently used to monitor environmental parameters such as ambient temperature, humidity, soil moisture, water level, among others. A mobile application intended to monitor irrigation-related parameters and to control solenoid valves for irrigation automation was developed in this study. The mobile application was written using Flutter software development kit, and the Dart programming language. The mobile application communicates with the cloud server using a REST API written in JavaScript. The data acquired from the cloud server are presented as the current sensor reading and graphs. On the other hand, the mobile application controls the solenoid valves by sending designated bytes of data to the cloud server. The mobile application developed in this study was designed to be integrated with both low-cost sensors and the Smartmesh IP sensors to enable real-time monitoring and data visualization, and facilitate irrigation scheduling and manual irrigation control. The mobile application developed in this study may be used for efficient irrigation water management of upland crop production systems and for agricultural modernization in the Philippines and other developing countries.

Evaluation of Factors Driving Post-Harvest Losses among Arable Crop Farmers in Ogbomoso, Oyo State, Nigeria

The development of crop coefficients (KC), the ratio of crop evapotranspiration (ETc) to reference evapotranspiration (ETo) is important for estimating irrigation water requirements in relation to specific crop phenological development. This research was conducted to determine growth-stage-specific Kc and crop water use for onion at Werer Agricultural Research Center, Middle Awash Valley Ethiopia during the main season (July–October) and during cool cropping seasons (March–June) from 2012-2015. Three non weighing lysimeters of 1.6 m x 1.6 m in surface area and 2 m deep were used to measure crop water use and local weather data were used to determine the reference evapotranspiration (ETo). The results showed that the obtained Kc values for main cropping season planted onion during initial, crop development, mid season and late-season stages were 0.57, 0.78, 1.03 and 0.77 respectively. Meanwhile, the corresponding Kc values for cool cropping season planted onion were 0.49, 0.90, 1.01, and 0.79 in the respective growth stages. The seasonal crop evapotranspiration was 525.83mm and 465.57mm during main and cool cropping season respectively. The measured Kc values were significantly different from the FAO-56 reported values. Therefore, local calibration of crop coefficients is an essential for efficient irrigation water management and precise water applications. Keywords: onion, crop coefficient, growth stages, water balance, evapotranspiration, lysimeter DOI: 10.7176/JNSR/13-6-01 Publication date: March 31 st 2021

Seasonal Yield Response Factor for Red Bombay Onion (Allium cepa L.) Variety in Arba Minch, Ethiopia

Background: For better water resources management in the areas of water shortage for crop production, deficit irrigation is very important. The understanding of the yield response factor to water deficit is crucial for efficient irrigation water management. Deficit irrigation for studying yield response factors is always practiced in the way of stressing the demand of the crops. The present study was done for the determination of the seasonal yield response factor of red Bombay onion variety under Arba Minch agro climate condition. Furthermore, it also examined the effect of furrow irrigation systems on the seasonal yield response factor. Methods: The experiment was conducted from August to November 2019. The experiment had six treatments, which were the combination of two furrow irrigation systems and three irrigation levels. Data were collected for soil moisture before and after each irrigation and bulb yield. The seasonal yield response factors were determined through simple linear regressions using SPSS software. Result: When considering the furrow irrigation system as a factor, the seasonal yield response factor for alternate furrow irrigation system was 1.18 while for paired row furrow irrigation system was 1.07. This red Bombay variety of onion clearly shows more sensitive to water stress for alternative furrow irrigation systems than paired row furrow irrigation systems. Therefore, in the area of water shortage paired row furrow irrigation system is better than alternate furrow irrigation system. The seasonal yield response factor as a wall for red Bombay onion variety in Arba Minch agro-ecological condition was 1.12. Therefore, the red Bombay onion variety in Arba Minch agroclimate condition was sensitive to water stress.

Machine Learning Based Prediction of Reference Evapotranspiration (ET0) Using IoT

Accurate estimation of Reference Evapotranspiration (ET₀) is important for efficient management and conservation of irrigation water. Existing methods of ET₀ rate determination are complex for application at the farmer level. Apart from standard methods of ET₀ determination, many data-driven soft computing approaches were also proposed to determine the ET₀ with limited data set. We proposed a temperature and humidity-based ML approach for ET₀ rate determination on directly sensed environmental conditions of the crop field. Crop field environmental conditions for (ET₀) rate determination are sensed by the proposed Internet of Things (IoT) architecture. Crop field environmental conditions from the year 2015 to 2021 in Pakistan are used for the training and testing of the proposed model. Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Artificial Neural Network (ANN) based models are compared for performance. Crop fields directly sensed temperature and humidity pass to the model to train and predict the ET₀-rate of crop fields. The 10-fold cross-validation technique is applied for the evaluation of the proposed approach. The accuracy of the proposed solution for the ET₀ rate is compared against the Food and Agriculture Organization (FAO) recommended Penman-Monteith method for ET₀ rate determination. As concerned of the ML-based models the KNN model is more accurate as compared to SVM,GNB and ANN models with 92% accuracy. The KNN model of ET₀ is more efficient in reducing the Root Mean Squared Errors (RMSE) by 16% and Mean Absolute Errors (MAE) by 3% against the state of the art approach.